The Challenges of Latency
In any large-scale system, there are a few inescapable facts:
- A broad customer base will demand reasonably consistent performance across the globe.
- Business continuity will demand geographic diversity in your deployments.
- The speed of light isn't going to change.
The last point can't be emphasized enough. The speed of light dictates that even if we can route packets at the speed of light, seems unlikely, it will take 30ms for a packet to traverse the Atlantic.
Given these facts, latency is a critical part of every system architecture. Yet making latency tolerance a first order constraint in the architecture is not that common. The result is systems that become heavily influenced by the distance between deployments. Why is that a problem?
The Internet is a part of foundation of the global economy. Companies need to reliably reach their customers regardless of where they may be located. Architectures that force close geographic proximity of the components limit the quality of service provided to geographically distributed customers. Response time will obviously degrade the further customers are from the servers, but so will reliability. Despite the tremendous increase in the reliability of traffic routing on the Internet, the further you are from a service, the more often that service will be effectively unavailable to you.
Protecting the business against disasters also requires geographically diverse locations. Natural and manmade disasters can devastate a region. Power grids cover substantial portions of the US. Witness the sheer volume of landmass that was without power during grid shutdowns in the past 10 years. If your company generates revenue from your its web site, then the architecture needs to insure the site is up even in the face of localized or even broad regional events.
In this article, I will provide some guidelines and best practices for designing with latency as a first order constraint. One of the underlying principles is assuming high latency, not low latency. An architecture that is tolerant of high latency will operate perfectly well with low latency, but the opposite is never true.
The web has created an interaction style that is very problematic for building asynchronous systems. The web has trained the world to expect request/response interactions, with very low latency between the request and response. These expectations have driven architectures that are request/response oriented that lead to synchronous interactions from the browser to the data. This pattern does not lend itself to high latency connections.
Latency tolerance can only be achieved by introducing asynchronous interactions to your architecture. The challenge becomes determining the components that can be decoupled and integrated via asynchronous interactions. An asynchronous architecture is far more than simply changing the request/response from a call to a series of messages though. The client is still expecting a response in a deterministic time. Asynchronous architectures shift from deterministic response time to probabilistic response time. Removing the determinism is uncomfortable for users and probably for your business units, but is critical to achieving true asynchronous interactions.
Admittedly, it's impractical to decompose the typical web application into components that perform all external interactions asynchronously. But it should be possible to identify use cases that must support synchronous interactions and those that do not.
Let's consider the billing component of a web site. Billing is a secondary operation as far as the community member is concerned. They have something of value the site provides and billing is a necessary evil that allows them to retain access to the value. Most billing systems will provide the user with the ability to review their balance, see account history, and make payments. Behind the scenes, fees need to be assessed, invoices generated, and automated payments processed.
Account balance, history, and the payment initiation would most likely be web pages. It would be difficult to transform these interactions into an asynchronous flow, as the interactions with the users would become unnatural. Notice though, I said payment initiation. Here's an example of where the interaction and expectations can be changed to become more latency tolerant.
You can decompose your applications into a collection of loosely coupled components; expose your services using asynchronous interfaces, and yet still leave yourself parked in one data center with little hope of escape. You have to tackle your persistence model early in your architecture and require that data can be split along both functional and scale vectors or you will not be able to distribute your architecture across geographies. I recently read an article where the recommendation was to delay horizontal data spreading until you reach vertical scaling limits. I can think of few pieces of worse advice for an architect. Splitting data is more complex than splitting applications. But if you don't do it at the beginning, applications will ultimately take short cuts that rely on a monolithic schema. These dependencies will be extremely difficult to break in the future.
Partitioning data presents the modeler with several challenges. The most common is maintaining ACID compliant transactions across the data partitions. The traditional approach is to rely upon distributed transactions and two-phase commit. The problem with distributed transactions is they create synchronous couplings across the databases. Synchronous couplings are the antithesis of latency tolerant designs.
The alternative to ACID is BASE:
BASE frees the model from the need for synchronous couplings. Once you accept that state will not always be perfect and consistency occurs asynchronous to the initiating operation, you have a model that can tolerate latency. Of course there are situations where data needs to be consistent at the end of an operation. The CAP Theorem is a useful tool for determining what data to partition and what data must conform to ACID.
The CAP Theorem states that when designing databases you consider three properties, Consistency, Availability, and Partitioning. You can have at most two of the three for any data model. Organizing your data model around CAP allows you to make the appropriate decisions with regards to consistency and latency.
Design for Active/Active
If you do a good job with the preceding recommendations, then you've most likely created an architecture that can service your customers from all of your locations simultaneously. This architecture will serve your disaster recovery needs well as it is a more efficient and responsive approach than an active/passive pattern where only one location is serving traffic at a time. Utilization of your resources will be higher and by placing services nearer your customers, you are better meeting their needs as well. Additionally, active/active designs handle localized geographic events better as traffic can simply be rebalanced from the impacted data center to your remaining data centers. Business continuity is improved.
Traditional active/passive designs for disaster recovery have a complex testability problem that will often offset the challenges of an active/active design. Determining whether the passive data center is truly ready to take traffic can only be done by sending traffic to it. This is difficult to do in an active/passive design without impacting customers. Active/active has an inherent self-proof of suitability for managing traffic.
Any aspect of your architecture that is ignored will become a problem. This is a simple rule of doing design. As discussed in this article, latency is one aspect that must be considered. Decompose your architecture, taking systemic qualities into consideration. Decompose data, and give up on ACID levels of consistency. Assume that you will have highly latent connections between your components. If you take this approach, the result will be an architecture that is not only tolerant of broad geographic deployments, but is more responsive to your customers and more resilient to disasters.
About the Author
Dan Pritchett is a Technical Fellow at eBay where he has been a member of the architecture team for the past four years. In this role, Dan interfaces with the strategy, business, product and technology teams across eBay Marketplaces, PayPal and Skype. Dan has been at eBay since 2001 and has made significant contributions to various business critical initiatives at eBay, including the migration of attributes and catalogs to the V3 marketplace platform, the implementation of eBay's Global Billing System, and the introduction of reliable asynchronous processing to the eBay platform. With over 20 years of experience at technology companies such as Sun Microsystems, Hewlett Packard and Silicon Graphics, Dan has an extensive depth of technical experience, ranging from network-level protocols and operating systems to systems design and software patterns.
Dan has a B.S. in Computer Science from the University of Missouri, Rolla. He lives in San Jose, Ca. with his wife and two daughters. He likes to spend his free time coaching at Willow Glen Little League and tinkering with cantankerous old British cars.
Dan maintains a blog at www.addsimplicity.com.
Joost de Vries
Maybe the article is a bit terse for easy online reading.
I'll have to study this.
More on monolithic data....
Re: More on monolithic data....
Srini Penchikala Aug 21, 2014